rename csv path var name to match train function

Author: Krzysztof Czajkowski

timm/data/loader.py

@@ -229,7 +229,7 @@ def create_loader(
         worker_seeding: str = 'all',
         tf_preprocessing: bool = False,
         balance_classes: bool = False,
-        dataset_csv_path: Optional[str] = None 
+        samples_csv_path: Optional[str] = None 
 ):
     """
 
@@ -274,7 +274,7 @@ def create_loader(
         worker_seeding: Control worker random seeding at init.
         tf_preprocessing: Use TF 1.0 inference preprocessing for testing model ports.
         balance_classes: Sample classes with uniform probability
-        dataset_csv_path: Path to dataset csv, used for class balancing 
+        samples_csv_path: Path to dataset csv, used for class balancing 
 
     Returns:
         DataLoader
@@ -333,9 +333,9 @@ def create_loader(
     else:
         assert num_aug_repeats == 0, "RepeatAugment not currently supported in non-distributed or IterableDataset use"
         if balance_classes:
-            assert dataset_csv_path, "Provide csv with labels to use balance_classes."
-            dataset_csv = pd.read_csv(dataset_csv_path)
-            all_labels = dataset_csv["label"].values
+            assert samples_csv_path, "Provide csv with labels to use balance_classes."
+            samples_csv = pd.read_csv(samples_csv_path)
+            all_labels = samples_csv["label"].values
             unique, counts = np.unique(all_labels, return_counts=True)
             unique_counts = {v: c for v, c in zip(unique, counts)}
             label_weights = np.array([1 / unique_counts[num] for num in all_labels])